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• W2892213699 abstract "Autoregressive decoding is the only part of sequence-to-sequence models that prevents them from massive parallelization at inference time. Non-autoregressive models enable the decoder to generate all output symbols independently in parallel. We present a novel non-autoregressive architecture based on connectionist temporal classification and evaluate it on the task of neural machine translation. Unlike other non-autoregressive methods which operate in several steps, our model can be trained end-to-end. We conduct experiments on the WMT English-Romanian and English-German datasets. Our models achieve a significant speedup over the autoregressive models, keeping the translation quality comparable to other non-autoregressive models." @default.
• W2892213699 created "2018-09-27" @default.
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• W2892213699 date "2018-01-01" @default.
• W2892213699 modified "2023-10-16" @default.
• W2892213699 title "End-to-End Non-Autoregressive Neural Machine Translation with Connectionist Temporal Classification" @default.
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• W2892213699 doi "https://doi.org/10.18653/v1/d18-1336" @default.
• W2892213699 hasPublicationYear "2018" @default.
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